| Sumario: | This article discusses the use of [18F]FET PET/MR imaging and machine learning in the evaluation of glioma, the most common malignant tumors of the central nervous system. The article highlights the importance of molecular diagnostics in tumor characterization and the potential of radiomics, which analyzes and quantifies imaging data, to provide valuable biological information. The study presented in the article focuses on the role of MR multiparametric radiomics in predicting tumor residual derived from [18F]FET PET/MR imaging data in patients with glioma. The results show that the radiomics models, particularly the nomogram, have the capacity to predict tumor residual and can potentially improve the prognosis and treatment of glioma patients. The article also mentions other studies that have explored the relationship between [18F]FET PET and MR imaging in glioma patients using machine learning techniques. Overall, this research suggests that the combination of hybrid imaging methods and machine learning has the potential to enhance the prognostic, diagnostic, and therapeutic outcomes for glioma patients.
|
|---|